docstr: Return two tensors, of shape
  (batch_size, num_samples, max_prediction_length, target_dim)
  and (batch_size, num_samples) respectively.
docstr_tok: ["Return","two","tensors",",","of","shape","(","batch_size",",","num_samples",",","max_prediction_length",",","target_dim",")","and","(","batch_size",",","num_samples",")","respectively","."]
code: def hybrid_forward(
      self, F, past_target: Tensor, past_valid_length: Tensor
  ) -> Tuple[Tensor, Tensor]:
      """
      Return two tensors, of shape
      (batch_size, num_samples, max_prediction_length, target_dim)
      and (batch_size, num_samples) respectively.
      """
      batch_size = past_target.shape[0]
      assert past_valid_length.shape[0] == batch_size
  
      target_shape = (self.num_parallel_samples, batch_size, 25)
      pred_target = nd.stack(
          nd.random.uniform(shape=target_shape),
          nd.random.randint(0, 10, shape=target_shape).astype(
              np.float32
          ),
          axis=-1,
      )
      pred_valid_length = nd.random.randint(
          15, 25 + 1, shape=target_shape[:2]
      )
  
      return pred_target, pred_valid_length
  
file_path: gluonts/test/mx/model/tpp/test_tpp_predictor.py
imports: from typing import Tuple
  import mxnet
  import numpy
  import pandas
  from pandas.tseries.frequencies import to_offset
  import pytest
  from mxnet import nd
  from gluonts.mx import PointProcessGluonPredictor, PointProcessSampleForecast
  from gluonts.mx import Tensor
  from gluonts.transform import ContinuousTimeInstanceSplitter, ContinuousTimePredictionSampler
  from .common import point_process_dataset, point_process_dataset_2
label_desc: Use image node_id 3 for calling the MockTPPPredictionNet obj's underlying member method code with example usage: obj.hybrid_forward(F, past_target, past_valid_length) and returns: pred_target,  pred_valid_length